Computer-aided method-development programs require accurate models to describe retention and to make predictions based on a limited number of scouting gradients. The performance of five different retention models for hydrophilic-interaction chromatography (HILIC) is assessed for a wide range of analytes. Gradient-elution equations are presented for each model, using Simpson’s Rule to approximate the integral in case no exact solution exists. For most compound classes the adsorption model, i.e. a linear relation between the logarithm of the retention factor and the logarithm of the composition, is found to provide the most robust performance. Prediction accuracies depended on analyte class, with peptide retention being predicted least accurately, and on the stationary phase, with better results for a diol column than for an amide column. The two-parameter adsorption model is also attractive, because it can be used with good results using only two scanning gradients. This model is recommended as the first-choice model for describing and predicting HILIC retention data, because of its accuracy and linearity. Other models (linear solvent-strength model, mixed-mode model) should only be considered after validating their applicability in specific cases.

计算机辅助方法开发程序需要精确的模型来描述保留率，并基于有限数量的探查梯度进行预测。针对多种分析物评估了五种不同的亲水相互作用色谱（HILIC）保留模型的性能。在没有精确解的情况下，使用辛普森法则对每个模型给出了梯度洗脱方程。对于大多数化合物类别，发现吸附模型（即，保留因子的对数与组合物的对数之间的线性关系）提供了最可靠的性能。预测的准确性取决于分析物的类别，其中肽保留的预测最不准确，而取决于固定相，二醇柱的结果优于酰胺柱的结果。两参数吸附模型也很有吸引力，因为仅需使用两个扫描梯度即可使用它，并获得良好的结果。由于其准确性和线性性，建议将该模型作为描述和预测HILIC保留数据的首选模型。其他模型（线性溶剂强度模型，混合模式模型）仅应在验证其在特定情况下的适用性后考虑。